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Что (кто) такое supervised - определение
MACHINE LEARNING TASK OF LEARNING A FUNCTION THAT MAPS AN INPUT TO AN OUTPUT BASED ON EXAMPLE INPUT-OUTPUT PAIRS
Supervised classification; Supervised machine learning; Supervised Machine Learning; Fully-supervised machine learning; Applications of supervised learning; Algorithms for supervised learning; Generative training
Найдено результатов: 157
Supervised
·Impf & ·p.p. of Supervise.
Supervised learning
Supervised learning (SL) is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.Stuart J.
Supervised injection site
![Naloxone, a drug on hand at clinics used to administer in cases of [[opioid overdose]]](https://commons.wikimedia.org/wiki/Special:FilePath/NaloxoneKit.jpg?width=200 "Naloxone, a drug on hand at clinics used to administer in cases of [[opioid overdose]]")
MEDICAL FACILITY
Safe injection sites; Injecting room; Safer injection facility; Supervised injection facilities; Safe injection site; Supervised injection sites; Fix room; Harm reduction center; Supervised Consumption Site; Supervised injection facility
Supervised injection sites (SIS) are medically supervised facilities designed to provide a hygienic environment in which people are able to consume illicit recreational drugs intravenously and prevent deaths due to drug overdoses. The legality of such a facility is dependent by location and political jurisdiction.
Weak supervision
MACHINE LEARNING APPROACH WHERE NOISY, LIMITED, OR IMPRECISE SOURCES ARE USED TO PROVIDE SUPERVISION SIGNAL FOR LABELING LARGE AMOUNTS OF TRAINING DATA IN A SUPERVISED LEARNING SETTING
Semi-supervised learning; Semi supervised learning; Semisupervised learning; Weak supervison; Semi-supervised machine learning; Semi-Supervised Learning
Weak supervision is a branch of machine learning where noisy, limited, or imprecise sources are used to provide supervision signal for labeling large amounts of training data in a supervised learning setting. This approach alleviates the burden of obtaining hand-labeled data sets, which can be costly or impractical.
Supervised diver
A SCUBA DIVE EDUCATION LEVEL ACCORDING TO ISO 24801-1
Supervised Diver
Supervised diver specifies the training and certification for recreational scuba divers in international standard ISO 24801-1 and the equivalent European Standard EN 14153-1. Various diving organizations offer diving training that meets the requirements of the Supervised Diver.
Semi-supervised learning
MACHINE LEARNING APPROACH WHERE NOISY, LIMITED, OR IMPRECISE SOURCES ARE USED TO PROVIDE SUPERVISION SIGNAL FOR LABELING LARGE AMOUNTS OF TRAINING DATA IN A SUPERVISED LEARNING SETTING
Semi-supervised learning; Semi supervised learning; Semisupervised learning; Weak supervison; Semi-supervised machine learning; Semi-Supervised Learning
Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Semi-supervised learning falls between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data).
Clinical supervision
DISCUSS CASEWORK AND OTHER PROFESSIONAL ISSUES IN A STRUCTURED WAY WITH ANOTHER PROFESSIONAL IN THE FIELD OF COUNSELING
Medical supervision; Medically supervised
Supervision is used in counselling, psychotherapy, and other mental health disciplines as well as many other professions engaged in working with people. Supervision may be applied as well to practitioners in somatic disciplines for their preparatory work for patients as well as collateral with patients.
Supervised psychoanalysis
Supervising analyst; Psychoanalysis under supervision; Supervisory analyst
A supervised psychoanalysis or psychoanalysis under supervision is a form of psychoanalytic treatment in which the psychoanalyst afterwards discusses the psychological content of the treatment, both manifest and latent, with a senior, more experienced colleague.Roger Perron, Supervised analysis
United States federal probation and supervised release
CONCEPT FROM US CRIMINAL LAW
Internet restrictions as conditions of supervised release under United States federal law; Conditions of supervised release under United States federal law; Supervised release under United States federal law; Federal supervised release; Supervised release under U.S. federal law; Revocation table; Probation and supervised release under United States federal law; Federal probation and supervised release; Class A felony; Class B felony; Class C felony; Class D felony; Class E felony
United States federal probation and supervised release are imposed at sentencing. The difference between probation and supervised release is that the former is imposed as a substitute for imprisonment, or in addition to home detention, while the latter is imposed in addition to imprisonment.
supervision
ACT OR INSTANCE OF DIRECTING, MANAGING, OR OVERSIGHT
Supervisory; Supervising
n.
1) to exercise supervision of, over
2) to tighten supervision
3) to ease up on, relax supervision
4) lax, slack; strict supervision
5) under smb.'s supervision
Википедия
Supervised learning
Supervised learning (SL) is a machine learning paradigm for problems where the available data consists of labeled examples, meaning that each data point contains features (covariates) and an associated label. The goal of supervised learning algorithms is learning a function that maps feature vectors (inputs) to labels (output), based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias). This statistical quality of an algorithm is measured through the so-called generalization error.
